B Cooper

Optimizing 4D cone beam computed tomography acquisition by varying the gantry velocity and projection time interval.

Four dimensional cone beam computed tomography (4DCBCT) is an emerging clinical image guidance strategy for tumour sites affected by respiratory motion. In current generation 4DCBCT techniques, both the gantry rotation speed and imaging frequency are constant and independent of the patients breathing which can lead to projection clustering. We present a mixed integer quadratic programming (MIQP) model for respiratory motion guided-4DCBCT (RMG-4DCBCT) which regulates the gantry velocity and projection time interval, in response to the patients respiratory signal, so that a full set of evenly spaced projections can be taken in a number of phase, or displacement, bins during the respiratory cycle. In each respiratory bin, an image can be reconstructed from the projections to give a 4D view of the patients anatomy so that the motion of the lungs, and tumour, can be observed during the breathing cycle. A solution to the full MIQP model in a practical amount of time, 10 s, is not possible with the leading commercial MIQP solvers, so a heuristic method is presented. Using parameter settings typically used on current generation 4DCBCT systems (4 min image acquisition, 1200 projections, 10 respiratory bins) and a sinusoidal breathing trace with a 4 s period, we show that the root mean square (RMS) of the angular separation between projections with displacement binning is 2.7° using existing constant gantry speed systems and 0.6° using RMG-4DCBCT. For phase based binning the RMS is 2.7° using constant gantry speed systems and 2.5° using RMG-4DCBCT. The optimization algorithm presented is a critical step on the path to developing a system for RMG-4DCBCT.

Respiratory triggered 4D cone‐beam computed tomography: A novel method to reduce imaging dose

PURPOSE A novel method called respiratory triggered 4D cone-beam computed tomography (RT 4D CBCT) is described whereby imaging dose can be reduced without degrading image quality. RT 4D CBCT utilizes a respiratory signal to trigger projections such that only a single projection is assigned to a given respiratory bin for each breathing cycle. In contrast, commercial 4D CBCT does not actively use the respiratory signal to minimize image dose. METHODS To compare RT 4D CBCT with conventional 4D CBCT, 3600 CBCT projections of a thorax phantom were gathered and reconstructed to generate a ground truth CBCT dataset. Simulation pairs of conventional 4D CBCT acquisitions and RT 4D CBCT acquisitions were developed assuming a sinusoidal respiratory signal which governs the selection of projections from the pool of 3600 original projections. The RT 4D CBCT acquisition triggers a single projection when the respiratory signal enters a desired acquisition bin; the conventional acquisition does not use a respiratory trigger and projections are acquired at a constant frequency. Acquisition parameters studied were breathing period, acquisition time, and imager frequency. The performance of RT 4D CBCT using phase based and displacement based sorting was also studied. Image quality was quantified by calculating difference images of the test dataset from the ground truth dataset. Imaging dose was calculated by counting projections. RESULTS Using phase based sorting RT 4D CBCT results in 47% less imaging dose on average compared to conventional 4D CBCT. Image quality differences were less than 4% at worst. Using displacement based sorting RT 4D CBCT results in 57% less imaging dose on average, than conventional 4D CBCT methods; however, image quality was 26% worse with RT 4D CBCT. CONCLUSIONS Simulation studies have shown that RT 4D CBCT reduces imaging dose while maintaining comparable image quality for phase based 4D CBCT; image quality is degraded for displacement based RT 4D CBCT in its current implementation.

Improving thoracic four-dimensional cone-beam CT reconstruction with anatomical-adaptive image regularization (AAIR).

Total-variation (TV) minimization reconstructions can significantly reduce noise and streaks in thoracic four-dimensional cone-beam computed tomography (4D CBCT) images compared to the Feldkamp-Davis-Kress (FDK) algorithm currently used in practice. TV minimization reconstructions are, however, prone to over-smoothing anatomical details and are also computationally inefficient. The aim of this study is to demonstrate a proof of concept that these disadvantages can be overcome by incorporating the general knowledge of the thoracic anatomy via anatomy segmentation into the reconstruction. The proposed method, referred as the anatomical-adaptive image regularization (AAIR) method, utilizes the adaptive-steepest-descent projection-onto-convex-sets (ASD-POCS) framework, but introduces an additional anatomy segmentation step in every iteration. The anatomy segmentation information is implemented in the reconstruction using a heuristic approach to adaptively suppress over-smoothing at anatomical structures of interest. The performance of AAIR depends on parameters describing the weighting of the anatomy segmentation prior and segmentation threshold values. A sensitivity study revealed that the reconstruction outcome is not sensitive to these parameters as long as they are chosen within a suitable range. AAIR was validated using a digital phantom and a patient scan and was compared to FDK, ASD-POCS and the prior image constrained compressed sensing (PICCS) method. For the phantom case, AAIR reconstruction was quantitatively shown to be the most accurate as indicated by the mean absolute difference and the structural similarity index. For the patient case, AAIR resulted in the highest signal-to-noise ratio (i.e. the lowest level of noise and streaking) and the highest contrast-to-noise ratios for the tumor and the bony anatomy (i.e. the best visibility of anatomical details). Overall, AAIR was much less prone to over-smoothing anatomical details compared to ASD-POCS and did not suffer from residual noise/streaking and motion blur migrated from the prior image as in PICCS. AAIR was also found to be more computationally efficient than both ASD-POCS and PICCS, with a reduction in computation time of over 50% compared to ASD-POCS. The use of anatomy segmentation was, for the first time, demonstrated to significantly improve image quality and computational efficiency for thoracic 4D CBCT reconstruction. Further developments are required to facilitate AAIR for practical use.

The first implementation of respiratory triggered 4DCBCT on a linear accelerator.

Four dimensional cone beam computed tomography (4DCBCT) is an image guidance strategy used for patient positioning in radiotherapy. In conventional implementations of 4DCBCT, a constant gantry speed and a constant projection pulse rate are used. Unfortunately, this leads to higher imaging doses than are necessary because a large number of redundant projections are acquired. In theoretical studies, we have previously demonstrated that by suppressing redundant projections the imaging dose can be reduced by 40-50% for a majority of patients with little reduction in image quality. The aim of this study was to experimentally realise the projection suppression technique, which we have called Respiratory Triggered 4DCBCT (RT-4DCBCT). A real-time control system was developed that takes the respiratory signal as input and computes whether to acquire, or suppress, the next projection trigger during 4DCBCT acquisition. The CIRS dynamic thorax phantom was programmed with a 2 cm peak-to-peak motion and periods ranging from 2 to 8 s. Image quality was assessed by computing the edge response width of a 3 cm imaging insert placed in the phantom as well as the signal to noise ratio of the phantoms tissue and the contrast to noise ratio between the phantoms lung and tissue. The standard deviation in the superior-inferior direction of the 3 cm imaging insert was used to assess intra-phase bin displacement variations with a higher standard deviation implying more motion blur. The 4DCBCT imaging dose was reduced by 8.6%, 41%, 54%, 70% and 77% for patients with 2, 3, 4, 6 and 8 s breathing periods respectively when compared to conventional 4DCBCT. The standard deviation of the intra-phase bin displacement variation of the 3 cm imaging insert was reduced by between 13% and 43% indicating a more consistent position for the projections within respiratory phases. For the 4 s breathing period, the edge response width was reduced by 39% (0.8 mm) with only a 6-7% decrease in the signal to noise and contrast to noise ratios. RT-4DCBCT has been experimentally realised and reduced to practice on a linear accelerator with a measurable imaging dose reductions over conventional 4DCBCT and little degradation in image quality.

Quantifying the image quality and dose reduction of respiratory triggered 4D cone-beam computed tomography with patient-measured breathing.

Respiratory triggered four dimensional cone-beam computed tomography (RT 4D CBCT) is a novel technique that uses a patients respiratory signal to drive the image acquisition with the goal of imaging dose reduction without degrading image quality. This work investigates image quality and dose using patient-measured respiratory signals for RT 4D CBCT simulations. Studies were performed that simulate a 4D CBCT image acquisition using both the novel RT 4D CBCT technique and a conventional 4D CBCT technique. A set containing 111 free breathing lung cancer patient respiratory signal files was used to create 111 pairs of RT 4D CBCT and conventional 4D CBCT image sets from realistic simulations of a 4D CBCT system using a Rando phantom and the digital phantom, XCAT. Each of these image sets were compared to a ground truth dataset from which a mean absolute pixel difference (MAPD) metric was calculated to quantify the degradation of image quality. The number of projections used in each simulation was counted and was assumed as a surrogate for imaging dose. Based on 111 breathing traces, when comparing RT 4D CBCT with conventional 4D CBCT, the average image quality was reduced by 7.6% (Rando study) and 11.1% (XCAT study). However, the average imaging dose reduction was 53% based on needing fewer projections (617 on average) than conventional 4D CBCT (1320 projections). The simulation studies have demonstrated that the RT 4D CBCT method can potentially offer a 53% saving in imaging dose on average compared to conventional 4D CBCT in simulation studies using a wide range of patient-measured breathing traces with a minimal impact on image quality.

TH-E-17A-06: Anatomical-Adaptive Compressed Sensing (AACS) Reconstruction for Thoracic 4-Dimensional Cone-Beam CT

PURPOSE The Feldkamp-Davis-Kress (FDK) algorithm currently used for clinical thoracic 4-dimensional (4D) cone-beam CT (CBCT) reconstruction suffers from noise and streaking artifacts due to projection under-sampling. Compressed sensing theory enables reconstruction of under-sampled datasets via total-variation (TV) minimization, but TV-minimization algorithms such as adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS) often converge slowly and are prone to over-smoothing anatomical details. These disadvantages can be overcome by incorporating general anatomical knowledge via anatomy segmentation. Based on this concept, we have developed an anatomical-adaptive compressed sensing (AACS) algorithm for thoracic 4D-CBCT reconstruction. METHODS AACS is based on the ASD-POCS framework, where each iteration consists of a TV-minimization step and a data fidelity constraint step. Prior to every AACS iteration, four major thoracic anatomical structures - soft tissue, lungs, bony anatomy, and pulmonary details - were segmented from the updated solution image. Based on the segmentation, an anatomical-adaptive weighting was applied to the TV-minimization step, so that TV-minimization was enhanced at noisy/streaky regions and suppressed at anatomical structures of interest. The image quality and convergence speed of AACS was compared to conventional ASD-POCS using an XCAT digital phantom and a patient scan. RESULTS For the XCAT phantom, the AACS image represented the ground truth better than the ASD-POCS image, giving a higher structural similarity index (0.93 vs. 0.84) and lower absolute difference (1.1*104 vs. 1.4*104 ). For the patient case, while both algorithms resulted in much less noise and streaking than FDK, the AACS image showed considerably better contrast and sharpness of the vessels, tumor, and fiducial marker than the ASD-POCS image. In addition, AACS converged over 50% faster than ASD-POCS in both cases. CONCLUSIONS The proposed AACS algorithm was shown to reconstruct thoracic 4D-CBCT images more accurately and with faster convergence compared to ASD-POCS. The superior image quality and rapid convergence makes AACS promising for future clinical use.

SU-E-J-183: Quantifying the Image Quality and Dose Reduction of Respiratory Triggered 4D Cone-Beam Computed Tomography with Patient- Measured Breathing

PURPOSE Respiratory triggered four dimensional cone-beam computed tomography (RT 4D CBCT) is a novel technique that uses a patients respiratory signal to drive the image acquisition with the goal of imaging dose reduction without degrading image quality. This work investigates image quality and dose using patient-measured respiratory signals for RT 4D CBCT simulations instead of synthetic sinusoidal signals used in previous work. METHODS Studies were performed that simulate a 4D CBCT image acquisition using both the novel RT 4D CBCT technique and a conventional 4D CBCT technique from a database of oversampled Rando phantom CBCT projections. A database containing 111 free breathing lung cancer patient respiratory signal files was used to create 111 RT 4D CBCT and 111 conventional 4D CBCT image datasets from realistic simulations of a 4D RT CBCT system. Each of these image datasets were compared to a ground truth dataset from which a root mean square error (RMSE) metric was calculated to quantify the degradation of image quality. The number of projections used in each simulation is counted and was assumed as a surrogate for imaging dose. RESULTS Based on 111 breathing traces, when comparing RT 4D CBCT with conventional 4D CBCT the average image quality was reduced by 7.6%. However, the average imaging dose reduction was 53% based on needing fewer projections (617 on average) than conventional 4D CBCT (1320 projections). CONCLUSION The simulation studies using a wide range of patient breathing traces have demonstrated that the RT 4D CBCT method can potentially offer a substantial saving of imaging dose of 53% on average compared to conventional 4D CBCT in simulation studies with a minimal impact on image quality. A patent application (PCT/US2012/048693) has been filed which is related to this work.

WE‐G‐134‐07: Respiratory Motion Guided Four Dimensional Cone Beam Computed Tomography: Image Quality Analysis

PURPOSE The aim of Respiratory Motion Guided-4DCBCT (RMG-4DCBCT) is to improve image quality and reduce radiation dose in 4DCBCT imaging. In current generation 4DCBCT, the gantry rotation speed and imaging frequency are constant and independent of the patients breathing which leads to projection clustering. We have developed a software system which regulates the gantry velocity and projection time interval, in response to the patients real time respiratory signal, so that a full set of near-evenly spaced projections can be acquired for each respiratory phase. We present our results analysing the quality of images produced with RMG-4DCBCT using the Catphan phantom. METHODS The RMG-4DCBCT software: (1) optimizes the gantry trajectory and projection pulse rate schedule based on the patients predicted breathing trace, (2) adjusts the gantry velocity and projection pulse rate according to the patients real time breathing signal and (3) sends commands to the gantry and kilovoltage imager. Acquisition of 60, RMG-4DCBCT(60), and 120, RMG-4DCBCT(120), half fan projections in each of 10 respiratory phases was simulated in real-time using 111 breathing traces from 24 lung cancer patients. A dataset containing 608 half fan projections of the Catphan phantom was sampled to reconstruct 4DCBCT images using both conventional 4DCBCT and RMG-4DCBCT. From the reconstructed images a streak ratio (SR) was calculated to quantify image quality. RESULTS The average SR was reduced from 5.16 with conventional 4DCBCT to 1.5 with RMG-4DCBCT(120) and 2.9 with RMG-4DCBCT(60). The average time required to acquire the dataset was 240 seconds with conventional 4DCBCT, 599 seconds for RMG-4DCBCT(120) and 260 seconds for RMG-4DCBCT(60). If we force RMG-4DCBCT to acquire 120 projections in exactly 240 seconds the SR is 4.2. CONCLUSION There are less streak artifacts with RMG-4DCBCT than conventional 4DCBCT. The imaging time for RMG-4DCBCT depends on the patients breathing rate and number of projections acquired. This project is supported by an NHMRC Australia Fellowship and NHMRC project grant 1034060.

SU‐C‐213CD‐05: Respiratory Signal Triggered 4D Cone‐Beam Computed Tomography on a Linear Accelerator

Purpose: To compare imaging dose and image quality for a commercially available 4D CBCT system and a proposed respiratory signal triggered 4D CBCT system which uses fewer projections and thus can reduce imaging dose burden. Methods: A large original set of full‐fan kilovoltage projection images of a stationary thorax phantom was acquired through 200 degrees of gantry rotation. This dataset was reconstructed to produce a ‘ground truth’ CBCT dataset (GT). Using acquisition parameters from a commercially available 4D CBCT system, the original projections were resampled to produce a 4D CBCT dataset (A). A sine wave respiratory signal, with period 4 seconds, is assumed and used to split projections into phase bins. Respiratory signal triggered 4D CBCT uses the same acquisition parameters, but the respiratory signal is used to trigger projection acquisition so that only one projection per phase bin is acquired (dataset B). For the commercial 4D CBCT system (A) and the respiratory signal triggered 4D CBCT system (B), image quality was quantified using the root mean squared deviation (RMSD) of the pixel values from the ground truth (GT) dataset. Imaging dose was assessed assuming one projection equals one dose unit. Results: The imaging dose for 4D CBCT system (A) was 118 dose units compared to the proposed 4D CBCT system (B) 58 dose units. The RMSD was calculated as 105.4, std. dev. 5.4 and 107.5, std. dev. 4.0 for the 4D CBCT system (A) and the proposed 4D CBCT system (B) respectively. Conclusions: The respiratory signal triggered 4D CBCT system exhibits a significantly reduced dose compared to the commercial 4D CBCT system. This comes at a small reduction in image quality. Future work will be to investigate the performance of the proposed 4D CBCT system with real respiratory signals. This work has been partially funded by the National Health and Medical Research Council, Australia(funding application ID APP1034060).